This paper proposes a trajectory tracking controller based on adaptive neural networks (ANNs) for robot manipulators (RMs) to achieve the high precision position tracking performance. In this controller, adaptive radial basis function (RBF) neural networks control is investigated to control the joints position and approximate the unknown dynamics of an n-link robot manipulators. The adaptive RBF network can effectively improve the control performance against large uncertainty of the system. The online adaptive control training laws are determined by Lyapunov stability and the approximation theory, so that uniformly stable adaptation is guaranteed, and asymptotically tracking is achieved. In adition, a robust control is constructed as an auxiliary controller to suppress the neural network modeling errors and the bounded disturbances to guarantee the stability and robustness under various environments such as the mass variation, the external disturbances and modeling uncertainties. Finally, simulation examples are given to illustrate the effectiveness of the proposed approach control system for two link-robot manipulators. From simulation results, we can find that the proposed adaptive control has fast reduction rate in tracking errors and tracking errors is converged to zero when t → ∞. Moreover, when the tracking errors reach the big value, there is little chattering in torque.
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